{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Matplotlib Tutorial for Beginners"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Matplotlib?\n",
    "\n",
    "- Matplotlib is a 2D python plotting library (similar interface with Matlab)\n",
    "- Compatible with both NumPy and Pandas for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZWEHNTA4W+Usa69SfCah1NGvTaauAziwcYOtzR0Q2EIv0ZJ8DjoisAQHIx1oj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d0qBhkEIajpHJpJea6IyVIPlGQQvvFwa9moT8LfHvafYzMBaF2Nq0WZBtDeO0hrL8Ryly6yvLUC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\nRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual representation of Pandas\n",
    "\n",
    "import os\n",
    "from IPython.display import Image\n",
    "PATH = \"F:\\\\Github\\\\Python tutorials\\\\Introduction to Matplotlib\\\\\"\n",
    "Image(filename = PATH + \"Matplotlib logo2.png\", width=600, height=300)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial Overview\n",
    "\n",
    "- What is matplotlib and how/why it's used\n",
    "- Line Graphs using NumPy\n",
    "- Scatter Plots using NumPy\n",
    "- Bar Charts & Subplots wsing Numpy\n",
    "- Histogram using NumPy\n",
    "- Line & Bar Graphs using Pandas df\n",
    "- Scatterplot Using DFs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing / Installing packages\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Packages / libraries\n",
    "import os #provides functions for interacting with the operating system\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# To install Pandas type \"pip install matloplib\" to the anaconda terminal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Line Graphs using Matplotlib & Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating an array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating 2 arrays\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Having more than 1 line - Adding Title, legends and x/y tickers\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Scatter Plots using Matplotlib & Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simple Example\n",
    "\n",
    "# np.arange: from 0 to the number you specify\n",
    "# np.random.randint: From -> To ,Number of numbers you need\n",
    "# np.random.randn(): random numbers 0-1 with normal distribution (mean 0 and variance 1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 2 - Scatter with size\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Bar Plots using Matplotlib & Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 1\n",
    "\n",
    "# Creating the raw data\n",
    "\n",
    "\n",
    "# Plotting it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 2 - Subplots\n",
    "\n",
    "# Creating the raw data\n",
    "\n",
    "\n",
    "# Plotting it\n",
    "\n",
    "\n",
    "# bar\n",
    "\n",
    "\n",
    "# line\n",
    "\n",
    "\n",
    "# scatter\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Example 1 \n",
    "\n",
    "# creating the raw data\n",
    "\n",
    "\n",
    "# plotting\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Lineplots using Pandas' DFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This is to find out your current directory\n",
    "# cwd = os.getcwd()\n",
    "# cwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the data\n",
    "\n",
    "# runs all the data\n",
    "\n",
    "#runs the first 5 rows\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 1 - Simple line graph\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 2 - Line graphs with xtickers\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 3 - Line graphs with xtickers and less data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 4 - 2+ data points on the graph\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 5 - Combo graphs\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 6 - Combo stack bars\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 7 - Combo 2+ bar graphs\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Scatterplot Using DFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 1 - Simple Scatterplot\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 2 - Scatterplot with groups\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 3 - Scatterplot with groups\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example 4 - Combo bar graph with calculated field\n",
    "\n"
   ]
  }
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